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28th Pacific Symposium on Biocomputing, PSB 2023

作者： Ayati, Marzieh Yilmaz, Serhan Blasco Tavares Pereira Lopes, Filipa Chance, Mark Koyuturk, Mehmet Department Of Computer Science University Of Texas Rio Grande Valley EdinburgTX United States Department Of Computer And Data Sciences ClevelandOH United States Department Of Nutrition ClevelandOH United States Center For Proteomics And Bioinformatics ClevelandOH United States Case Comprehensive Cancer Center Case Western Reserve University ClevelandOH United States

ISBN: (纸本)9789811270604

Protein phosphorylation is a key post-translational modification that plays a central role in many cellular processes. With recent advances in biotechnology, thousands of phosphorylated sites can be identified and quantified in a given sample, enabling proteome-wide screening of cellular signaling. However, for most (> 90%) of the phosphorylation sites that are identified in these experiments, the kinase(s) that target these sites are unknown. To broadly utilize available structural, functional, evolutionary, and contextual information in predicting kinase-substrate associations (KSAs), we develop a network-based machine learning framework. Our framework integrates a multitude of data sources to characterize the landscape of functional relationships and associations among phosphosites and kinases. To construct a phosphosite-phosphosite association network, we use sequence similarity, shared biological pathways, co-evolution, co-occurrence, and co-phosphorylation of phosphosites across different biological states. To construct a kinase-kinase association network, we integrate protein-protein interactions, shared biological pathways, and membership in common kinase families. We use node embeddings computed from these heterogeneous networks to train machine learning models for predicting kinase-substrate associations. Our systematic computational experiments using the PhosphositePLUS database shows that the resulting algorithm, NetKSA, outperforms two state-of-the-art algorithms, including KinomeXplorer and LinkPhinder, in overall KSA prediction. By stratifying the ranking of kinases, NetKSA also enables annotation of phosphosites that are targeted by relatively less-studied kinases. Availability: The code and data are available at ***/NetKSA/. © 2022 The Authors.

关键词： Association reactions

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data mining for epidemiology: The correlation of typhoid fever occurrence and environmental factors 7

Data mining for epidemiology: The correlation of typhoid fev...

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7th International Conference on Computer science and Computational Intelligence, CSCI 2022

作者： Asadi, Faisal Trinugroho, Joko Pebrianto Hidayat, Alam Ahmad Rahutomo, Reza Pardamean, Bens Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta11480 Indonesia Information System Department School of Information System Bina Nusantara University Jakarta11480 Indonesia Computer Science Department BINUS Graduate Program-Master of Computer Science Program Bina Nusantara University Jakarta 11480 Indonesia

Typhoid fever is an endemic disease that burdens Indonesia and has a potentially fatal infection multisystem. Salmonella typhi bacterium is responsible for typhoid fever disease. Poor sanitation, crowding, and slums are the main factors of increasing typhoid fever incidences. Environmental factors directly connected to meteorological factors are the main factor in breeding the Salmonella typhi bacterium. This study aims to identify the correlation between meteorological parameters and typhoid fever disease occurrence. The study was carried out in Jakarta, Indonesia, and the Bureau of Meteorological, Climatology, and Geophysics (BMKG) provided the meteorological parameter data. In addition, the Jakarta health surveillance office provided information on typhoid fever hospitalizations from 2019 to 2021. Pearson's concept was utilized d to investigate the correlation between typhoid fever incidences and the meteorological parameters. Humidity, precipitation, and wind speed are the meteorological parameters that significantly affect in contribute to the occurrence of typhoid fever disease. These findings might be used as a reference for Indonesia's government in making public policy to prevent typhoid fever in Indonesia. © 2022 Elsevier B.V.. All rights reserved.

关键词： data mining

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POS1052 PREDICTING FIBROMYALGIA OUTCOME FROM A RHEUMATIC-SPECIFIC SURVEY

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Annals of the Rheumatic Diseases 2024年 83卷 573-574页

作者： D. Selani L. Qin F. Zegers L. Klareskog E. Steyerberg S. Le Cessie B. Axnäs M.J.T. Reinders E.B. Van den Akker R. Knevel Delft University of Technology Delft Bioinformatics Lab Pattern Recognition and Bioinformatics Delft Netherlands Leiden University Medical Center Department of Rheumatology Leiden Netherlands Leiden University Medical Center Department of Biomedical Data Sciences Leiden Netherlands Karolinska Institute Stockholm Sweden Leiden University Medical Center Department of Clinical Epidemiology Leiden Netherlands Elsa Science AB Stockholm Sweden Delft University of Technology Delft Bioinformatics Lab Delft Netherlands

Background: Around 3% of the global population suffers from Fibromyalgia (FM) and the average diagnostic delay is six years [1]. It is a hard disease to diagnose as it has symptoms that are common in non-Inflammatory and inflammatory diseases [2]. An outcome classification based on self-reported symptoms could benefit patients, by allowing patients to be referred to specialists sooner. “Rheumatic?” is an online symptom checker originally designed to differentiate patients with immune-mediated from non-immune-mediated rheumatic disease [3], yet it might also aid in the early identification of patients at risk of FM. A previous machine-learning approach trained on “Rheumatic?” data performed moderately well at diagnosis prediction task, with an AUROCS of 0.54 – 0.79 [3]. Objectives: We investigate whether self-reported symptoms collected from “ Rheumatic?” can be used to identify FM diagnosis. Methods: “Rheumatic?” includes a baseline questionnaire about current symptoms and past diagnoses, along with three follow-up surveys (at 3,6 and 12 months) about healthcare visits, medication, and further diagnoses. We selected participants that entered the “Rheumatic?” between July 2021 and July 2022. Each participant’s baseline data was projected into an embedding (Figure 1A) using a pre-trained auto-encoder model. The embedding was then used, along with FM outcomes as labels, to train an XG-Boost classifier (Figure 1B). The data was split 80:20 into a train set and test set. The train set was used to train the auto-encoder model in an unsupervised manner, and the classifier in a supervised manner. The test set was used to get the final model performance metrics. We used the Area Under the Receiver Operating Curve Score (AUROCS) to assess model performance. A separate pipeline was created for every follow-up timepoint. Results: The number of participants at each follow-up changed as not every participant replied to a follow-up survey (Table 1). As expected, participants who r

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Machine Learning Approaches in Detecting Autism Spectrum Disorder

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Procedia Computer science 2023年 227卷 1070-1076页

作者： Daniel Nicholas Dominic Tjeng Wawan Cenggoro Bens Pardamean Computer Science Department BINUS Graduate Program – Master of Computer Science Program Bina Nusantara University Jakarta Indonesia 11480 Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia 11480 Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia 11480

Early detection of Autism Spectrum Disorder (ASD) needs to be increased to prevent further adverse impacts. Thus, the classifi-cation between ASD and Typically Development (TD) individuals is an intriguing task. This review study has collected 26 related papers to answer four research questions, i.e., what are the most used data inputs, brain atlases, and machine learning models for ASD classification, as also to discover the significant parts of the brain correlated with the ASD. It was eventually found that functional connectivity matrix, Support Vector Machine, and CC200 are the most frequently used data input, model, and brain atlas, respectively. Researchers also concluded that the posterior temporal fusiform cortex, intracalcarine cortex, cuneal cortex, subcallosal cortex, occipital pole, and lateral occipital cortex are the brain regions highly correlated with ASD.

关键词： autism spectrum disorder brain atlas brain region machine learning

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Sentiment Analysis for TikTok Review Using VADER Sentiment and SVM Model

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Procedia Computer science 2023年 227卷 168-175页

作者： Mahmud Isnan Gregorius Natanael Elwirehardja Bens Pardamean Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta 11480 Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta 11480 Indonesia Computer Science Department BINUS Graduate Program – Master of Computer Science Program Bina Nusantara University Jakarta 11480 Indonesia

TikTok, a social networking site for uploading short videos, has become one of the most popular. Despite this, not all users are happy with the app; there are criticisms and suggestions, one of which is reviewed via the TikTok app on the Google Play Store. The reviews were extracted and then used for training a sentiment analysis model. The VADER sentiment method was utilized to offer the review's initial labeling (positive, neutral, and negative). The result revealed that most reviews were classified as positive, meaning that the data were imbalanced and challenging to handle in further analysis. Therefore, Random Under-sampling (RUS) and Random Over-sampling (ROS) methods were deployed to deal with that condition. The labeled reviews were subsequently pre-processed using tools such as case folding, noise removal, normalization, and stopwords before being used for training a Support Vector Machine (SVM) model for sentiment classification. The SVM trained without resampling produced the most favorable results, with an F1-score of 0.80.

关键词： Sentiment Analysis TikTok SVM VADER Sentiment Google Play Store

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APIR:Aggregating Universal Proteomics database Search Algorithms for Peptide Identification with FDR Control

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Genomics, Proteomics & bioinformatics 2024年第2期22卷 171-187页

作者： Yiling Elaine Chen Xinzhou Ge Kyla Woyshner MeiLu McDermott Antigoni Manousopoulou Scott B.Ficarro Jarrod A.Marto Kexin Li Leo David Wang Jingyi Jessica Li Department of Statistics and Data Science University of CaliforniaLos AngelesCA 90095USA Department of Immuno-Oncology Beckman Research InstituteCity of Hope National Medical CenterDuarteCA 91010USA Department of Quantitative and Computational Biology University of Southern CaliforniaLos AngelesCA 90089USA Department of Cancer Biology and Blais Proteomics Center Dana-Farber Cancer InstituteDepartment of PathologyBrigham and Women’s Hospital and Harvard Medical SchoolBostonMA 02215USA Department of Pediatrics City of Hope National Medical CenterDuarteCA 91010USA Bioinformatics Interdepartmental Program University of CaliforniaLos AngelesCA 90095USA Department of Human Genetics University of CaliforniaLos AngelesCA 90095USA Department of Computational Medicine University of CaliforniaLos AngelesCA 90095USA Department of Biostatistics University of CaliforniaLos AngelesCA 90095USA

Advances in mass spectrometry(MS)have enabled high-throughput analysis of proteomes in biological *** state-of-the-art MS data analysis relies on database search algorithms to quantify proteins by identifying peptide–spectrum matches(PSMs),which convert mass spectra to peptide *** database search algorithms use distinct search strategies and thus may identify unique ***,no existing approaches can aggregate all user-specified database search algorithms with a guaranteed increase in the number of identified peptides and a control on the false discovery rate(FDR).To fill in this gap,we proposed a statistical framework,Aggregation of Peptide Identification Results(APIR),that is universally compatible with all database search ***,under an FDR threshold,APIR is guaranteed to identify at least as many,if not more,peptides as individual database search algorithms *** of APIR on a complex proteomics standard dataset showed that APIR outpowers individual database search algorithms and empirically controls the *** data studies showed that APIR can identify disease-related proteins and post-translational modifications missed by some individual database search *** APIR framework is easily extendable to aggregating discoveries made by multiple algorithms in other high-throughput biomedical data analysis,e.g.,differential gene expression analysis on RNA sequencing *** APIR R package is available at https://***/yiling0210/APIR.

关键词： Shotgun proteomics Peptide–spectrum match Peptide identification Aggregation of lists FDR control

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Ontology-based representation and analysis of conditional vaccine immune responses using Omics data 14

Ontology-based representation and analysis of conditional va...

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14th International Conference on Biomedical Ontologies, ICBO 2023

作者： Huffman, Anthony Ong, Edison Brunson, Tim Sanati, Nasim Zheng, Jie Masci, Anna Maria Wu, Guanming He, Yongqun Department of Computational Medicine and Bioinformatics University of Michigan Ann ArborMI United States GlaxoSmithKline Rixensart1330 Belgium Oregon Health & Science University PortlandOR United States Department of Genetics University of Pennsylvania Perelman School of Medicine PhiladelphiaPA United States Office of Data Science National Institute of Environmental Health Science DurhamNC United States Unit of Laboratory Animal Medicine University of Michigan Ann ArborMI United States Department of Microbiology and Immunology University of Michigan Ann ArborMI United States Center for Computational Medicine and Bioinformatics University of Michigan Ann ArborMI United States

ImmPort, the world’s largest repository of immunology data, includes many vaccine immune response datasets. ImmPort maps the metadata of these studies to ontology and database schema. As of February 28, 2023, our ImmPort data analysis identified 6.258 immune exposures using 47 vaccines in 4,607 human subjects, and 324 cohort studies from the ImmPort. We hypothesized that an integrative ontological representation of the data from these studies would enhance our understanding and analysis of these ImmPort vaccine studies, and with ontological classification and tools such as VIGET, we could further study the effects of different conditions such as vaccine types and host biological sex on the vaccine response gene expression profiles. Our Vaccine Ontology (VO) analysis classified these 37 vaccines into bacterial, viral, and protozoan vaccine types with different vaccine properties. The ImmPort metadata types were modeled with the Vaccine Investigation Ontology (VIO). Our new ontology-based pipeline extracted vaccine response data from the ImmPort database, annotated them based on ontology, obtained corresponding gene expression data from the GEO, and performed consistent omics data analysis. Our use case found gene profiles shared and differed from live and killed inactivated influenza vaccines. Furthermore, our Omics data analysis using the VIGET tool found that female and male human subjects have differential host responses for influenza vaccines. For example, our study showed much stronger early female responses to influenza vaccination than males, and males was able to show active immune responses at a later stage. Interestingly, the female (but not male) human subject group also showed significantly enriched neutrophil degranulation at Day 3 after influenza vaccination;however, males (but not females) displayed significantly enriched neutrophil degranulation at Day 14 after influenza vaccination. These mechanisms have been used to find differences between the gene

关键词： Ontology

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Early senescence of pancreatic β cells induced by unfolded protein response deficiency prevents type 1 diabetes

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Journal of Zhejiang University-science B(Biomedicine & Biotechnology) 2024年第9期25卷 796-799页

作者： Haipeng CHENG Zhenwang ZHAO Dan LIU Yufei WANG Min ZHANG Department of Pathology the Second Xiangya HospitalCentral South UniversityChangsha410011China Institute of Cardiovascular Disease Key Laboratory for Arteriosclerology of Hunan ProvinceHunan International Scientific and Technological Cooperation Base of Arteriosclerotic DiseaseDepartment of Bioinformatics and Medical Big DataHengyang Medical SchoolUniversity of South ChinaHengyang421001China School of Basic Medicine Health Science CenterHubei University of Arts and ScienceXiangyang441053China Guangxi Key Laboratory of Diabetic Systems Medicine Guilin Medical UniversityGuilin541199China Medical Education Department Guangdong Provincial People’s HospitalZhuhai Hospital(Jinwan Central Hospital of Zhuhai)Zhuhai519041China

Type 1 diabetes(T1D)is a T lymphocyte-mediated autoimmune disease caused by pancreaticβ-cell destruction,which eventually leads to reduced insulin level and increased blood glucose level(Syed,2022).As a multifactorial disease,T1D is characterized by a genetic predisposition associated with various environmental and cellular elements(Syed,2022).Pancreaticβcells have long been considered the"innocent victims"in T1D pathogenesis since the pancreas is attacked by the immune cells,resulting in a process known as insulitis,in which the immune cells infiltrate pancreatic islets and secrete pro-inflammatory ***,growing evidence suggests that variousβ-cell stresses,dysfunction,and death contribute to T1D pathogenesis,as it has been observed thatβ-cell dysfunction in autoantibody-positive(Aabt)individuals exists long before T1D diagnosis(Evans-Molina et al.,2018).

关键词： diagnosis diabetes pathogenesis

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Systematic Literature Review of Transformer Model Implementations in Detecting Depression

Systematic Literature Review of Transformer Model Implementa...

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International Conference of Computer and Informatics Engineering (IC2IE)

作者： Kenjovan Nanggala Gregorius Natanael Elwirehardja Bens Pardamean Computer Science Department BINUS Graduate Program - Master of Computer Science Bina Nusantara University Jakarta Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia

This systematic literature review explores the application of transformer models in early detection of human depression, encompassing text, audio, and video data modalities. Transformer architectures, notably BERT for text, have proven adept at capturing crucial contextual and linguistic patterns associated with depression. For audio and video data, hybrid approaches that combine transformer models with other architectures are prevalent. Key features considered include eye gaze, head pose, facial muscle movements, and audio characteristics such as MFCC and Log-mel Spectrogram, along with text embeddings. Performance comparisons underscore the superiority of text-based data in consistently delivering the most promising results, followed by audio and video modalities when utilizing transformer models. The fusion of multiple modalities emerges as an effective strategy for enhancing predictive accuracy, with the amalgamation of audio, video, and text data yielding the most precise outcomes. However, it is noteworthy that unimodal approaches also exhibit potential, with text data exhibiting superior performance over audio and video data. Nevertheless, several challenges persist in this research domain, including imbalanced datasets, the limited availability of comprehensive and diverse samples, and the inherent complexities in interpreting visual cues. Addressing these challenges remains imperative for the continued advancement of depression detection using transformer-based models across various modalities.

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A Systematic Literature Review of Deep Learning Application in Multiclass Anomaly Detection for Chest Medical Imaging

A Systematic Literature Review of Deep Learning Application ...

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International Conference of Computer and Informatics Engineering (IC2IE)

作者： Philips Bens Pardamean Gregorius Natanael Elwirehardja Mahmud Isnan Computer Science Department BINUS Graduate Program - Master of Compuer Science Bina Nusantara University Jakarta Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia

Medical imaging abnormality detection is challenging, but deep learning approaches have shown promise. This paper reviews the current state of the art in deep learning approaches for detecting abnormalities in chest medical imaging. To discover the trends, opportunities, and challenges associated with this field, 18 studies were selected from Google Scholar based on their titles, abstracts, and contents for extensive review to answer two research questions. The study found that the National Institutes of Health (NIH) Chest X-ray 14 dataset is the most used dataset for this task. Most research uses a single-modal approach, considering only image data as input, with X-ray being the more popular instrument. There are 8 out of 18 studies leverage the transfer learning approach, with ResN et50 being the most popular network. MobileNetV2 has demonstrated competitive results compared to more robust networks. Preprocessing techniques such as image enhancement and data augmentation are leveraged by 61.1 % of the reviewed studies and are shown to improve model performance.

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